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The Influence of Citizenship Norms, Efficacy Belief, and Parents’ Participation on
Students’ Civic Engagement in Nigerian Universities:
Data Screening and Preliminary Analysis
Ahmadu, Talatu Salihu
Awang Saleh Graduate School of Arts and Sciences, Universiti Utara Malaysia
06010 Sintok, Kedah Darul Aman, Malaysia; Corresponding author: Tass2004@yahoo.com
Prof. Madya Dr. Yahya Bin Don
Awang Saleh Graduate School of Arts and Sciences, Universiti Utara Malaysia, 06010 Sintok
Kedah Darul Aman, Malaysia; Email:d.yahya@uum.edu.my
Dr. Ismail Hussein Hamzat
Awang Saleh Graduate School of Arts and Sciences, Universiti Utara Malaysia, 06010 Sintok
Kedah Darul Aman, Malaysia; Email:ihussein@uum.edu.my
Doi:10.5901/jesr.2016.v6n2p81
Abstract
This article explored the data collected regarding the influence of citizenship norms, efficacy belief, and parents’ participation
on students’ civic engagement in Nigeria. Five hundred and eighty-four (584) students from eight universities located in northwest geopolitical zone of Nigeria completed a self administered questionnaire. The questionnaire is a 5- Likert-scale. The data
was analyzed by means of Statistical Package for the Social Sciences (SPSS) package version 20. However, we conducted
the initial screening and analysis of data so as to fulfil the assumptions specified for multivariate analysis. That is, the values
missed out by respondents were treated. Then comes the assessment and handling of outliers, which are the disproportionate
case scores that is expected to have a substantial negative impact on the outcomes. Following this, a test of normality, linearity
and multicollinearity were performed. It was then concluded that the data was fit for further multivariate analysis..
Keywords: Citizenship Norms, Efficacy Belief, Parents’ Participation, Students’ Civic Engagement; Data Screening
1. Introduction
A preliminary screening of data is extremely vital in any multivariate analysis. This is because it helps researchers to
recognize any possible violations of the main assumptions concerning the application of multivariate techniques of data
analysis (Hair, Money, Samouel, & Page, 2007). In addition, initial data screening makes researchers to comprehend the
data collected better for further analysis. Until recently, some scholars (Hair, Black, Babin, & Anderson, 2010) observed
that scientific investigations have been conducted mostly without initial data screening and preliminary analysis perhaps
due to the trouble attributed to it. Such oversight is a serious problem (Chernick,2008) because, overlooking of the initial
data screening would increase the standard error estimates, which in turn influences the regression-based path
coefficient negatively (Dijkstra, 1983; Ringle, Sarstedt, & Straub, 2012).
Consequently, to fill in this practical gap, the present study intended at screening the data gathered on the effect of
citizenship norms, efficacy beliefs, and parents’ involvement on civil engagement among young university individuals in
Nigeria. Afterwards, the data is subjected to check out the missed values for analysis, assessment of outliers, normality
test, linearity and multicollinearity assessment as noted by (Tabachnick, & Fidell, 2007; Hair et al., 2013). Given this, the
article is structured under the following section: The early section focuses review of literature on the area under
discussion. The next part highlighted how the methodology used in the present study was conducted, followed by the
results offered in the other segment of the paper. While the last section draws conclusion on the findings of the study.
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Literature Review
1.1.1 Civic Engagement
Numerous factors have been studied to depict why citizens are involved in civic activities of the society. Some of the
main factors include norms of citizenship, civic knowledge, self-efficacy, open classroom climate, volunteerism, pro-social
values and activities, and political socialization (Dalton, 2008; Manganelli et al., 2014; Richardson, 2003; Campbell,
2008; Acharya et al., 2010a; Amnå, Ekström, Kerr, & Stattin, 2009). Thus, civic engagement are those activities that
focused people’s attitudes, main beliefs, opinions, as well as behaviors that indicate the circumstances which are
external to personal and communal networks (Amna, 2012). Although a review of civic engagement literature indicated
that there is lack of agreement regarding, not only in the terminology used, and the different theoretical perspectives, but
also the definitions offered are considered to be of a similar construct (Wilkenfeld, Lauckhardt, & Torney- Purta, 2010).
Despite the aforementioned empirical studies on these antecedents little is known on the links among norms of
citizenship, parents’ participation, efficacy beliefs, and youths’ involvement in civil activities.
Since the factors responsible for people’s involvement in civil activities is an outcome of their perception about
good citizenship, the democracy theory (Thompson, 1970 ) is mainly helpful in illuminating the civic norms which
resulted to participatory acts in various activism. Scholars like (Dalton, 2008; Theiss-Morse, & Hibbing, 2005; Soja,
Innocent, & Abuh, 2014) have utilized the theory to elucidate the role of citizens in a democratic environment. Hence,
established that the norms of citizenship have made people to become aware and get involved in both voluntary and civil
activities as ways of performing their civic responsibility. In other words, a person’s conviction (efficacy belief) is gradually
affecting him to transform his culture so as to make life more habitable for the generality of people.
1.1.2 Norms of Citizenship and Civic Engagement
Educational psychologists have generally agreed that civic knowledge and norms, play an important role in promoting
adolescents’ expectations to participate in civic activities, such as voting, participating in a peaceful rally or march, and
online discussion of politics(Galston, 2007; Manganelli, Lucidi, & Alivernini, 2014; Torney-Purta, 2002). Norms of
citizenship are the views held by citizens as what it meant for a person to be a good individual (Copeland, 2014).
citizenship might shape the expectations of peoples’ roles in civic and political processes. This is because citizenship
identifies what is expected of the individual and what the individual expects of government, it influences a series of
people’s thinking and ultimately determine what prompted their actions in civil affairs ( Kotzian,2014).
However, only a limited empirical research has investigated the role of citizenship norm as one of the vital factors
that can promote young people’s participation in civic activities. Such neglect has been unfortunate because to a greater
extent, civic norm directly influences young adults’ decisions whether to engage in or stay away from civic activities.
Hence, civic norm is central to the accomplishment of civic engagement or participation. Although some researchers are
divided on which citizenship norms (duty or engaged norms) is prominent. Contrarily, Dalton (2008) argued that
citizenship norms explained that the notion people have about disengagement in civic activities is misconstrued. Thus,
citizenship norms impact on people in different ways (Zaff, Malanchuk, & Eccles, 2008).
1.1.3 Parents' Participation
In addition to norms of citizenship, parents' participation in civic activities is alleged to persuade young adults' decision to
be engaged in schools' civic activities. Parents are seen to demonstrate as models in the process of socializing their
offspring. Basically, children learn the dos and don'ts of their parents, which in turn affects their behaviour within the
society at large. Children naturally seek to be accepted by their family and society (McClelland, 1987; Packer, 2008).
Young adults who have a strong need for affiliation enjoy being part of a group and tend to conform to the group's norms
in order to be accepted by other members of the group (Christensen, Rothgerber, Wood, & Matz, 2004; McClelland,
1987; Packer, 2008; Smith & Mackie, 2007; Smith, Hogg, Martin, & Terry, 2007). Hence, it is reasonable to expect that
youths' participation in civic activities could be shaped by their parents. Moreover, because of its theoretical importance,
parents' real participation in civic activities has been suggested as an important predictor of youths' decision to engage in
civic activities. However, only a few studies have examined the significant role of parents in determining youths’ decision
to engage in civic activities.
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1.1.4 Efficacy Belief
Relevant literatures also indicate that self-efficacy is a well-established factor that plays a significant role in
understanding human behaviour because it shapes those opinions and actions they exhibited. Bandura, (2010) has
pointed out that self- efficacy help to explain people’s beliefs in their ability to influence events that affect their lives.
Hence, a comprehensive review of literature in educational and social psychology suggests that self-efficacy influences
various attitudinal and behavioural outcomes such as academic achievement(Burgoon, Meece, & Granger, 2012;
Caprara, Vecchione, Alessandri, Gerbino, & Barbaranelli, 2011), health-related behaviours (French, 2013; Jones,
Renger, & Kang, 2007), and work-related performance (Judge, Jackson, Shaw, Scott, & Rich, 2007; Sadri & Robertson,
1993; Walumbwa, Lawler, Avolio, Peng Wang, & Kan Shi, 2005).
However, only a handful of studies have explored the link between efficacy beliefs and civic engagement and or
participation. Hence, this is another gap in the literature. Consequently, understanding the role of political efficacy
(efficacy belief), the feeling that one’s political action have, or can have, to influence the political process (Zmerli, 2010) in
civic engagement is significant because (Morrell, 2005) explained that individuals who have soaring political efficacy are
prone to engage in political participation, such as conversations that require them to argue before making conscious
decision. In other words, efficacy beliefs predict changes in students’ actual political attitudes and behaviours.
2. Methodology
2.1
Sample
Data was obtained from a sample of five hundred and eighty-four (584) students resulting in a response rate of 85%.
Young learners from eight (8) federal and state universities sited in the North-west Nigeria offered courses in citizenship
education in line with the curriculum policy of University education. Universities were drawn using stratified probability
sampling technique Ahmadu Bello University (ABU), Zaria; Kaduna State University (KSU) and Nigeria Defense
Academy Kaduna. Kano University of Science and Technology Wudil (KUT); Bayero University are sited in kano state.
Then comes Kebbi State University of Science and Technology Aliero; Umaru Musa Yar'adua University Katsina, with
Usman Danfodio University Sokoto (UDUS) are among others.
2.2
Sample Characteristics
The characteristics examined are the gender, age, educational level of the respondents in university as well as the
university type of the respondents. The responses obtained showed that of 584 participants, majority of the respondents
in the sample; that is 394 (67.5%) were males, while the remaining 190, representing 32.5% were females. Majority of
participants 343 were above 26 years old (58.7%) followed by 226 (38.7%) students with age range between 21 to 25
years and the remaining 15 (2.6%) were below 20 years of age. In terms of education a high proportion of the
respondents 458 (78.4%) students were in federal universities while 126 (21.6%) were in state universities. A high
proportion of the respondents comprised of 300 level students, which accounted for 386 (66.1%) respondents. This is
followed by 98 respondents (approximately 16.8%) in 400 levels, while 91, representing 15.6% were in 200 levels. The
remaining 9 representing 1.5% were in 100 level.
2.3
Measures
We measured engaged citizenship norms using the subscale of Scheidegger and Staerkle (2011), a 5-item Likert scale
ranging from 1 (strongly disagree) to 5 (strongly agree). Examples of item include “I partake in student union in order to
influence civic decisions”. On the other hand, we also measured duty-based citizenship norms using items adapted like “I
always obey laws and regulations” from Citizenship, Involvement, Democracy Survey Howard, Gibson, & Stolle, (2005),
with 5-item Likert scale. While, efficacy belief was measured with adapted scale from Craig, Niemi, & Silver, (1990), a
five (5) item Likert which ranges from 1 – 5 respectively. "I know more about politics than most people my age" is an
example of the items. The last instrument (civic engagement), was an 8-item scale adapted from Schulz & Sibberns,
(2004) such as" I vote in national election", and measured with a 5-Likert scale.
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Technique
We used Statistical Package for Social Sciences (SPSS) application software version 20 to screen the data collected for
this study. The SPSS was developed by Nie & Hull (1968) and is one of the most commonly used programs for statistical
analysis in social science. However, this study adopted the SPSS because it is a complex method for analyzing data
(Pallant, 2011). In other words, it performs multifarious manoeuvring of data as well as analysis with simple command.
Besides, it accepts data from any sort of file and utilizes it for generating reports, providing features for plotting and
presentation of results.
3. Results and Discussion
Prior to the data screening, we used the SPSS- Statistical Package for Social Sciences version 18.0. which was most
popular for the task of cleaning data. Subsequently the screening commenced while centring on; missing value analysis;
assessment of outliers; normality test, linearity and multicollinearity test as recommended by Hair et al., (2010). Hence,
data must always be screened to ensure the data is reliable, as well as valid for testing the theory one intended to
extend.
3.1
Missing value analysis
Out of the 27,133 data points, 52 had randomly missing values in the original SPSS dataset, which accounted for 0.19%.
In particular, engaged citizenship norms (ECN03) and efficacy belief (EB03) have 6 missing values each. Similarly
engaged citizenship norms (ECN02 & 5) have 5 missing values each; ECN02 & duty citizenship norms (DCN03) have
4 missing values each; ECN01, ECN06, EB02, EB04, EB07, CVE05, CVE06, & PP04 have 2 missing values each;
EB06, EB09, CVE01, PP01, PP03,& PP05 have 1 missing value each and no missing value was found in civic
engagement (CVE03). There is no fixed percentage of missed values in a data set that makes a valid statistical
inference. Some researchers like (Schafer, 1999; Tabachnick & Fidell, 2007) have generally established that the rate of
5% or less of values missed is unimportant. Besides, researchers have recommended that the easiest way of replacing
missing values is mean substitution if the total percentage of missing data is 5% or less (Little, & Rubin, 1987; Raymond,
1986; Tabachnick, & Fidell, 2007). Therefore, randomly missing values were replaced using mean substitution in this
study, (Tabachnick, & Fidell, 2007). Table 1 shows the total and percentage of randomly missing values in the present
study.
Table 1. Result of Missing Values Identified and Replaced
S/No
Result Variable
Number of Replaced Missing Values
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
ECN01_1
ECN02_1
ECN03_1
ECN04_1
ECN05_1
ECN06_1
DCN03_1
EB02_1
EB03_1
EB04_1
EB06_1
EB07_1
EB09_1
CVE01_1
CVE03_1
CVE05_1
CVE06_1
PP01_1
PP03_1
2
5
6
4
5
2
4
2
6
2
1
2
1
1
0a
2
2
1
1
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20
21
PP04_1
2
PP05_1
1
Total
52 out of 27,133 data points
Percentage
0.19%
Note: Percentage of missing values is obtained by dividing the total number of
randomly missing values for the entire data set by total number of data points
multiplied by 100.
3.2
Assessment of outliers
Another important procedure for data screening is the estimation and treatment of outliers. These are the extreme case
scores that may probably have a considerable negative impact on the result of study (Barnett & Lewis, 1994). Thus, the
presence of outliers in the data set of a regression-based analysis can critically alter the estimates of regression
coefficients which lead to results that are not reliable (Verardi, & Croux, 2008). In order to distinguish any observation
which seems to be outside the SPSS value labels as a result of incorrect data entry, first and foremost, frequency tables
were tabulated for all variables using minimum and maximum statistics. Based on this initial analysis of frequency
statistics, there was no any value discovered to be outside the normal range.
Consequently, we used Mahalanobis distance (D2) to inspect the multivariate outliers. Thus, some scholars like
(Tabachnick & Fidell 2007) have attested that Mahalanobis Distance (D2) helped in identifying and dealing with
multivariate outlying cases. Based on thirty-four (34) observed variables of the study, the recommended threshold of chisquare is 65.25 (p = 0.001). Therefore, Mahalanobis values that exceeded this threshold were deleted. Given this
criterion, forty-seven (47) multivariate outliers were recognised and subsequently deleted from the dataset. This is
because the precision of data analysis technique might be affected. Consequently, after removing 47 multivariate outliers
the final dataset in this study was 584 as depicted in Table 2.
Table 2. Multivariate Outliers Detected and Deleted
Respondents Number
1
2
14
16
41
45
55
63
69
77
95
97
111
113
125
149
153
158
161
190
200
201
210
222
226
240
260
288
319
Mahalanobis Distance (D2)
66.78281
76.82827
69.44853
67.64753
76.36319
81.06928
73.88621
68.36067
81.22157
74.31133
98.25803
80.05467
75.5793
99.78491
68.71998
90.81231
82.44978
66.28306
99.25607
72.0938
104.31002
65.99075
67.06942
75.0931
66.34311
80.31723
68.6577
75.85014
93.12437
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338
67.41406
341
66.04293
342
74.54885
353
78.16664
358
78.73422
369
85.16338
377
171.5105
392
88.36981
396
66.11589
398
80.147
417
71.62666
423
71.01414
472
68.99674
481
74.77583
518
68.76815
523
80.00086
527
67.91333
539
67.64179
Note: N = 35; df = 34; X2 = 65.25; p = 0.001; D2 = • 65.25
3.3
Normality test
Traditionally researchers like (Reinartz, Haenlein, & Henseler, 2009; Wetzels, Odekerken-Schroder, & Van Oppen,
2009) assumed that PLS-SEM provided accurate model estimations in situations that are extremely non-normal.
Though, this postulation may turn out to be false. Hence, Hair, Sarstedt, Ringle & Mena (2012) suggested that normality
analysis should be done. Since, Tabachnick & Fidell, (2007); Hair et al., (2010) argued that normality is essentially a
way of making prediction in multivariate analysis. Chernick, (2008) clearly indicated that highly skewed or kurtoic data
has the tendency of inflating the bootstrapped standard error estimates thus affecting the path coefficients as noted by
Dijkstra, (1983) and Ringle et al., (2012). On this basis, the graphical method was used in this study to check for the
normality of data collected (Tabachnick, & Fidell, 2007).
It was suggested by Field, (2009) that in a large sample of 200 or more, it is more vital to look at the shape of the
distribution graphically rather than looking at the value of skewness or kurtosis. Explaining further, Field, (2009) stated
that a large sample reduces the standard errors; this in turn increases the value of the skewness and kurtosis statistics.
Thus, this justified the reason for using graphical method of normality test rather than the statistical methods. In line with
Field’s (2009) proposition, a histogram and normal probability plots were examined to certify that normality assumptions
have not been violated in this study. Figure 1 portrays the data in this study trail a normal pattern given that all the bars
on the histogram seem like a normal curve. Thus, Figure 1 shows that normality assumptions have not been violated in
the present study.
Figure 1 - Histogram and normal probability plots
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Multicollinearity Test
The occurrence of multicollinearity among the exogenous latent constructs according to Chatterjee, & Yilmaz, (1992);
and Hair, Black, Babin, Anderson, & Tatham, (2006) can significantly alter the estimates of regression coefficients and
their statistical significance tests. Multicollinearity means a situation in which one or more exogenous latent constructs
turn out to be extremely correlated. Specifically, Tabachnick, & Fidell, (2007) stated that multicollinearity increases the
standard errors of the coefficients. This in turn makes the coefficients statistically not important. In order to notice
multicollinearity, we employed two methods (Chatterjee, & Yilmaz, 1992; Peng, & Lai, 2012). In the first method, the
correlation matrix of the exogenous latent constructs was examined. Therefore, a correlation coefficient of 0.90 and over
indicates multicollinearity involving exogenous constructs (Hair, et al. 2010). Table 3 depicts the correlation matrix of all
exogenous latent constructs.
Table 3. Correlation matrix of the exogenous latent constructs
Variables
1
2
1
Engaged citizenship norms
1
2
Duty citizenship norms
.387**
1
.437**
3
Efficacy beliefs
.339**
-.345**
4
Parents' participation
-.342**
.517**
5
Civic engagement
.376**
Note: Correlation is significant at the 0.01 level (1-tailed).
3
4
5
1
-.598**
.466**
1
-.437**
1
Table 3 indicated the correlations between the exogenous latent constructs which were amply under the recommended
threshold values of 0.90. This revealed that the exogenous constructs were independent as well as not extremely
correlated. Thus, the indicators showed that they all have reached the satisfactory levels. Therefore, Hair, Ringle &
Sarstedt, (2011) reiterated that multicollinearity becomes a problem if VIF value is greater than 5, tolerance value is
smaller than 0.20, and more than 30 in case of condition index. Table 4 depicts the level of tolerance values, VIF values
and condition indices for the exogenous latent constructs.
Table 4. Tolerance and Variance Inflation Factors (VIF)
Independent Variables
Engaged citizenship norms
Duty citizenship norms
Efficacy beliefs
Parents' participation
Collinearity Statistics
Tolerance
VIF
.794
1.260
.741
1.349
.577
1.732
.618
1.618
Condition Index
6.288
13.151
16.403
28.818
However, It can be deduced from what obtains on table 4 that all the exogenous latent constructs are not extremely
correlated. Besides, the tolerance values is greater than 0.20. While, the VIF are well below .5 with condition indices
lower than 30 (Hair et al., 2014). As a result, the indicators showed no any signal to Collinearity problem.
3.5
Linearity
Linearity postulation indicates that a straight-line connection prevails between the independent and dependent variables.
Linearity is assessed by evaluating the shape of the scatteplots within the scatterplots matrices. Linearity assumption is
typically examined through the scatterplots of each exogenous constructs with the endogenous construct or partial
correlation plots between both independent and the dependent constructs. According to Mertler & Vannata, (2010), the
shape of the scatterplots should be elliptical thus substantiates the assumption of linearity (Hair et al., 2010).
4. Conclusion
Several empirical studies have been carried out with little or no concern for data screening and preliminary analysis.
However, disregarding such initial data screening may spell doom for the multivariate analysis, consequently, inflating the
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estimated standard error, for that reason, this study was presented to make known the implication of critical component
of multivariate analysis. Furthermore, it is vital to state that ensuring proper screening of data before the commencement
of multivariate analysis provides better insight to what obtains in the data. To put simply, when the researcher accurately
examines as well as treats the values missing, identifies outliers, normal data distribution with multicollinearity then the
task in analysis is half done. Bearing this in mind, it is evident that the preliminary assumptions of multivariate analysis
were not debased in this study. Thus, it can be said without doubt that the data was fit for further multivariate analyses,
the assessment of the measurement model as well as model structure.
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